Heaton, Heather A.

Abstract Real-time location systems (RTLS) such as radio frequency identification (RFID) have been shown to improve safety, save cost, and increase patient satisfaction in a healthcare setting especially in the emergency department (ED). Hospital administrators have realized the potential of these applications for improved workflow and operations and are positively adopting it despite the substantial implementation costs of such technologies. Our group has reported several studies with RFID data in the ED showing the relationship between RFID determined patient contact times and length of stay (LOS). In this study, we use ED RFID data to determine the relationship between the total LOS in ED to the overall patient satisfaction obtained from a survey report. ED survey results from 35 questions were obtained from patients after discharge. An observational cohort study was performed in the ED using RFID data from Jan 1 – Dec 31, 2017 matched with the ED survey participants. A total of 2595 survey data was used for logistic regression analysis with the RFID data. Results show that higher treatment LOS influenced overall patient satisfaction negatively. The results provide new insights into designing ideal patient-care team interactions to reduce overall LOS while providing optimal ED care, thereby improving patient satisfaction.

Abstract Background: Emergency Department Length of Stay (ED-LOS) is a crucial measurement for clinical and operational decisions and is used for regulatory reporting and research purposes. Accurate and standardized measurement of this duration improves the effectiveness of decisions and findings. Further, automated capture can eliminate dependence on human task-based data-entry. Objective: Compare a real-time location system (RTLS)-based measure of LOS against the current standard of practice (EMR); examine factors associated with discrepancy in LOS. Method: Estimation of LOS using the first and last RFID record in the RTL system, and statistical assessment of their similarity to the corresponding measurement from the EMR. Result: Length of stay durations from the two methods have very similar distributions, with a slight shift. Conclusion: RTLS provides an efficient and standardized means to measure LOS.

Abstract Objective The study sought to characterize the evaluation of patients who present following detection of an abnormal pulse using Apple Watch. Materials and Methods We conducted a retrospective review of patients evaluated for abnormal pulse detected using Apple Watch over a 4-month period. Results Among 264 included patients, clinical documentation for 41 (15.5%) explicitly noted an abnormal pulse alert. Preexisting atrial fibrillation was noted in 58 (22.0%). Most commonly performed testing included 12-lead echocardiography (n = 158; 59.8%), Holter monitor (n = 77; 29.2%), and chest x-ray (n = 64; 24.2%). A clinically actionable cardiovascular diagnosis of interest was established in only 30 (11.4%) patients, including 6 of 41 (15%) patients who received an explicit alert. Discussion False positive screening results may lead to overutilization of healthcare resources. Conclusions The Food and Drug Administration and Apple should consider the unintended consequences of widespread screening for asymptomatic (“silent”) atrial fibrillation and use of the Apple Watch abnormal pulse detection functionality by populations in whom the device has not been adequately studied.

Background Emergency Medicine Telehealth (TeleEM) represents an opportunity to work directly with referral centres, rural facilities and underserved areas to mitigate unnecessary testing, optimise resource utilisation and facilitate patient transfers across health systems. To optimise the impact of a TeleEM programme, a tool is needed to remotely monitor patient activity in multiple emergency department facilities, concurrently. Methods After identifying data sources for activation criteria put forth by the TeleEM operations group, rules were constructed within the electronic health record to facilitate data checks and ultimately produce a yes/no response if the category’s conditions were met. Responses were organised into a table, with functionality allowing end users to drill into the different sites to see patient-specific information for patients meeting activation criteria. Conclusions The TeleEM dashboard allows for proactive engagement by the TeleEM physician and strengthens the team-based approach of critically ill.